Ten Year House Price Volatility

I'm doing some work on reverse mortgages.  One of the issues confronting the analysis of reverse mortgages is long-term house price volatility.  While house prices can be quite volatile from year to year, this doesn't necessarily mean they are volatile for long-term holding periods.

Below are computations of 10 year, annualized, house price growth rates, standard deviations, minima, maxima, and coefficients of variation for the 100 largest US MSAs.  The data are the Federal Housing Finance Administration Purchase Only Index Data, and the computations are based on data from the first quarter of 1991 through the second quarter of 2018 (which means we have ten year hold data for 17+ years).  All data are nominal prices.

Note that in all cities, the average ten year growth rate is nominally positive.  The average across cities is 3.4 percent, with a range from .8 percent (Detroit) to 6 percent (San Francisco).  Neither of these should be surprising. 

More interesting (to me, anyway)  are the 42 cities that never had a negative house price period over a ten year hold.  Texas has a number of them (San Antonio has the maximum, minimum house price growth rate over ten years), and Pittsburgh and Oklahoma City are very steady too.  But a surprise to me are San Francisco and San Jose--markets that have has large short term drops in house prices.  In these markets, if one waited ten years, one never saw a house price drop over the holding period (again, we're talking in nominal terms here).  There have, however, been ten year periods in LA where nominal house price dropped by a shade under one percent per year.

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metro_name	andelta10	sdandelta10	minandelta10	maxandelta10	coefficient of variation
Akron, OH	0.015	0.019	-0.011	0.044	1.292
Albany-Schenectady-Troy, NY	0.036	0.025	0.001	0.072	0.695
Albuquerque, NM	0.028	0.018	-0.008	0.056	0.630
Allentown-Bethlehem-Easton, PA-NJ	0.029	0.029	-0.016	0.076	0.985
Anaheim-Santa Ana-Irvine, CA  (MSAD)	0.055	0.042	-0.004	0.132	0.772
Atlanta-Sandy Springs-Roswell, GA	0.025	0.024	-0.017	0.053	0.977
Austin-Round Rock, TX	0.050	0.011	0.025	0.073	0.226
Bakersfield, CA	0.029	0.041	-0.035	0.110	1.422
Baltimore-Columbia-Towson, MD	0.045	0.036	-0.010	0.100	0.781
Baton Rouge, LA	0.036	0.011	0.015	0.053	0.303
Birmingham-Hoover, AL	0.028	0.016	0.006	0.049	0.564
Boise City, ID	0.033	0.023	0.000	0.078	0.685
Boston, MA  (MSAD)	0.048	0.039	-0.005	0.105	0.795
Bridgeport-Stamford-Norwalk, CT	0.036	0.039	-0.021	0.093	1.080
Buffalo-Cheektowaga-Niagara Falls, NY	0.027	0.008	0.011	0.037	0.285
Cambridge-Newton-Framingham, MA  (MSAD)	0.046	0.035	-0.001	0.097	0.753
Camden, NJ  (MSAD)	0.033	0.035	-0.024	0.086	1.040
Cape Coral-Fort Myers, FL	0.031	0.045	-0.033	0.116	1.435
Charleston-North Charleston, SC	0.048	0.027	0.012	0.096	0.573
Charlotte-Concord-Gastonia, NC-SC	0.028	0.012	0.008	0.044	0.407
Chicago-Naperville-Arlington Heights, IL  (MSAD)	0.026	0.032	-0.018	0.070	1.219
Cincinnati, OH-KY-IN	0.020	0.017	-0.001	0.041	0.823
Cleveland-Elyria, OH	0.012	0.020	-0.013	0.042	1.660
Colorado Springs, CO	0.035	0.021	0.005	0.071	0.607
Columbia, SC	0.026	0.015	0.005	0.047	0.570
Columbus, OH	0.024	0.015	0.002	0.043	0.612
Dallas-Plano-Irving, TX  (MSAD)	0.035	0.011	0.012	0.054	0.305
Dayton, OH	0.011	0.014	-0.007	0.031	1.271
Denver-Aurora-Lakewood, CO	0.048	0.025	0.007	0.090	0.518
Detroit-Dearborn-Livonia, MI  (MSAD)	0.008	0.042	-0.052	0.067	5.142
El Paso, TX	0.028	0.016	-0.003	0.056	0.576
Elgin, IL  (MSAD)	0.016	0.029	-0.022	0.056	1.807
Fort Lauderdale-Pompano Beach-Deerfield Beach, FL  (MSAD)	0.044	0.045	-0.023	0.122	1.016
Fort Worth-Arlington, TX  (MSAD)	0.031	0.009	0.014	0.048	0.302
Fresno, CA	0.034	0.043	-0.032	0.110	1.293
Gary, IN  (MSAD)	0.021	0.012	0.004	0.038	0.577
Grand Rapids-Wyoming, MI	0.020	0.024	-0.014	0.053	1.197
Greensboro-High Point, NC	0.019	0.013	0.001	0.036	0.708
Greenville-Anderson-Mauldin, SC	0.028	0.009	0.012	0.042	0.334
Hartford-West Hartford-East Hartford, CT	0.030	0.029	-0.013	0.074	0.967
Honolulu ('Urban Honolulu'), HI	0.049	0.031	-0.011	0.093	0.622
Houston-The Woodlands-Sugar Land, TX	0.044	0.007	0.028	0.057	0.161
Indianapolis-Carmel-Anderson, IN	0.020	0.011	0.003	0.035	0.522
Jacksonville, FL	0.039	0.037	-0.015	0.099	0.959
Kansas City, MO-KS	0.027	0.021	0.001	0.054	0.779
Knoxville, TN	0.031	0.012	0.010	0.052	0.397
Lake County-Kenosha County, IL-WI  (MSAD)	0.019	0.029	-0.021	0.058	1.546
Las Vegas-Henderson-Paradise, NV	0.018	0.045	-0.041	0.095	2.524
Little Rock-North Little Rock-Conway, AR	0.027	0.012	0.007	0.043	0.432
Los Angeles-Long Beach-Glendale, CA  (MSAD)	0.053	0.046	-0.009	0.140	0.864
Louisville/Jefferson County, KY-IN	0.029	0.014	0.010	0.050	0.490
Memphis, TN-MS-AR	0.019	0.016	0.000	0.039	0.839
Miami-Miami Beach-Kendall, FL  (MSAD)	0.049	0.044	-0.014	0.126	0.896
Milwaukee-Waukesha-West Allis, WI	0.030	0.024	-0.005	0.059	0.825
Minneapolis-St. Paul-Bloomington, MN-WI	0.036	0.036	-0.008	0.083	0.994
Montgomery County-Bucks County-Chester County, PA  (MSAD)	0.039	0.028	-0.004	0.078	0.715
Nashville-Davidson--Murfreesboro--Franklin, TN	0.037	0.009	0.020	0.049	0.253
Nassau County-Suffolk County, NY  (MSAD)	0.052	0.044	-0.014	0.116	0.843
New Haven-Milford, CT	0.032	0.036	-0.022	0.086	1.118
New Orleans-Metairie, LA	0.038	0.017	0.010	0.066	0.435
New York-Jersey City-White Plains, NY-NJ  (MSAD)	0.048	0.040	-0.011	0.105	0.825
Newark, NJ-PA  (MSAD)	0.042	0.037	-0.013	0.096	0.887
North Port-Sarasota-Bradenton, FL	0.037	0.043	-0.027	0.117	1.172
Oakland-Hayward-Berkeley, CA  (MSAD)	0.047	0.048	-0.011	0.132	1.012
Oklahoma City, OK	0.035	0.008	0.022	0.049	0.236
Omaha-Council Bluffs, NE-IA	0.026	0.016	0.007	0.051	0.615
Orlando-Kissimmee-Sanford, FL	0.034	0.039	-0.023	0.105	1.163
Oxnard-Thousand Oaks-Ventura, CA	0.048	0.045	-0.015	0.127	0.935
Philadelphia, PA  (MSAD)	0.049	0.030	0.006	0.094	0.598
Phoenix-Mesa-Scottsdale, AZ	0.038	0.038	-0.017	0.107	0.997
Pittsburgh, PA	0.032	0.005	0.023	0.042	0.171
Portland-Vancouver-Hillsboro, OR-WA	0.047	0.019	0.020	0.075	0.402
Providence-Warwick, RI-MA	0.041	0.041	-0.019	0.100	0.996
Raleigh, NC	0.030	0.009	0.014	0.044	0.306
Richmond, VA	0.038	0.024	0.000	0.078	0.643
Riverside-San Bernardino-Ontario, CA	0.039	0.050	-0.026	0.133	1.275
Rochester, NY	0.020	0.007	0.010	0.031	0.358
Sacramento--Roseville--Arden-Arcade, CA	0.037	0.046	-0.026	0.119	1.246
Salt Lake City, UT	0.042	0.016	0.016	0.075	0.384
San Antonio-New Braunfels, TX	0.039	0.007	0.032	0.057	0.167
San Diego-Carlsbad, CA	0.052	0.046	-0.012	0.131	0.878
San Francisco-Redwood City-South San Francisco, CA  (MSAD)	0.060	0.033	0.011	0.118	0.547
San Jose-Sunnyvale-Santa Clara, CA	0.053	0.037	0.004	0.116	0.700
Seattle-Bellevue-Everett, WA  (MSAD)	0.049	0.025	0.017	0.096	0.519
Silver Spring-Frederick-Rockville, MD  (MSAD)	0.048	0.038	-0.011	0.109	0.790
St. Louis, MO-IL	0.030	0.023	-0.001	0.060	0.748
Stockton-Lodi, CA	0.026	0.051	-0.041	0.120	1.945
Syracuse, NY	0.025	0.014	0.005	0.049	0.566
Tacoma-Lakewood, WA  (MSAD)	0.039	0.029	-0.001	0.093	0.735
Tampa-St. Petersburg-Clearwater, FL	0.040	0.039	-0.017	0.111	0.966
Tucson, AZ	0.033	0.035	-0.026	0.088	1.070
Tulsa, OK	0.029	0.011	0.014	0.045	0.365
Virginia Beach-Norfolk-Newport News, VA-NC	0.043	0.034	-0.013	0.094	0.787
Warren-Troy-Farmington Hills, MI  (MSAD)	0.013	0.037	-0.038	0.063	2.848
Washington-Arlington-Alexandria, DC-VA-MD-WV  (MSAD)	0.052	0.035	-0.005	0.112	0.675
West Palm Beach-Boca Raton-Delray Beach, FL  (MSAD)	0.042	0.043	-0.021	0.120	1.026
Wichita, KS	0.024	0.012	0.010	0.041	0.473
Wilmington, DE-MD-NJ  (MSAD)	0.034	0.031	-0.016	0.081	0.905
Winston-Salem, NC	0.020	0.013	-0.001	0.038	0.664
Worcester, MA-CT	0.036	0.038	-0.016	0.093	1.051

Richard Florida on Choi, Green and Noh

He writes about what we write about on education, migration and rent.

It’s abundantly clear that in today’s economy, the ability to attract and mobilize highly educated people—so-called human capital—is the key factor in the the wealth of nations as well of that of cities. But the driving force of talent in economic growth also contributes to our worsening divides. While metropolitan areas with more educated people have higher levels of income, they also have higher housing costs. And the burden of those costs falls hardest on the less educated.
A working paper by urban economist Richard Green, of the University of Southern California, and Jung Choi, of the Urban Institute takes, a deep dive into this conundrum....

Ten things data have taught me about the world.

(1) Tax cuts do not magically create growth; 

(2) Vaccines are among the best things we have ever invented; 

(3) raising the minimum wage to a point improves living standards for low wage workers (and that point may be somewhere between $11 and $15 per hour), beyond that point, it lowers living standards for low wage workers; 

(4) GMOs are fine; 

(5) the benefits of the Clean Air Act swamp the costs by an order of magnitude or more; 

(6) the mortgage interest deduction has a vanishingly small impact on the homeownership rate; 

(7) trade has raised living standards for hundreds of millions around the world; 

(8) trade has reduced living standards for low skilled workers in the US; 

(9) rent control reduces the stock of rental housing; 

(10) even though I like Lebron better than Jordan, MJ was the better player.

Rent Stabilization Fails to Target Those in Need

Rent stabilization is a transfer from those who own rent stabilized units to those who live in such units.  As such, it is not a specific redistribution from high income households to low income households, but rather a random distribution from owners of various income levels (who can range from middle-class owners of one unit to large holders of private equity or REITS) to renters of various income levels.

I know of no good way to recover the incomes of property owners, but we can get a flavor of the distribution of income among beneficiaries of rent stabilized properties in Los Angeles, by looking at the income distribution of those who live in properties built just before rent stabilization and just after.  We can't exactly nail it, because rent stabilization in LA went into effect into effect in October 1978, and the census tells us the decade in when properties were being built.  Still, comparing the incomes of renters living in buildings built in the 1970s with those of the 1980s can tell us something about how well targeted rent stabilization is.

I downloaded American Community Survey data from IPUMS USA.   (See Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 7.0 [dataset]. Minneapolis, MN: University of Minnesota, 2017. 
https://doi.org/10.18128/D010.V7.0).  I looked at the city of Los Angeles, and stripped out single family detached houses, and, of course, owner houses.  I used the ACS Household Weights.  Here are the income distributions I found for properties built in the 1970s and 1980s.

Note that the median income of those in (largely) rent stabilized units is higher than those in units that are not stabilized.  Also note that the incomes at the 75th percentile are nearly the same.  At the 90th percentile, people in 1970s vintage properties have a lower income than those in 1980s properties, but their income is still rather high (i.e., it is a reasonable question to ask whether households who make $114,000 a year or more should be receiving a housing subsidy).

Taxing people of means (which we can identify) to provide housing subsidies to those without is good policy.  It is the correct way to help those whose income is insufficient to pay for adequate housing.

(p.s., whenever I post something like this, I welcome any and all attempts to reproduce it.  I makes mistakes!).